1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study - All LSOAs

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.

NB: no maps in the interests of speed

4.1 Data

We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

All analysis is at LSOA level. Cautions on inference from area level data apply.

4.2 CREDS place-based emmissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

  • Emissions are presented as per capita…
  • Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
##                       region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
##  1:                     <NA>    805          9834.584        2737.760
##  2:                     East   3392          8886.285        3133.982
##  3:            East Midlands   2713          7835.809        3195.006
##  4:                   London   4826          9116.160        3137.503
##  5:               North East   1634          6801.016        4123.301
##  6:               North West   4463          7430.665        2862.846
##  7:               South East   5278          9813.213        3648.334
##  8:               South West   3059          7930.219        2780.179
##  9:            West Midlands   3403          7506.665        4037.705
## 10: Yorkshire and The Humber   3271          7419.184        2772.234

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 2:    Test Valley 003B              St Mary's       2641        2487      2230
## 3:  Milton Keynes 017H              Broughton       2517        2382      2460
## 4:    Test Valley 003A                Alamein       2513        2638      2350
## 5:   Peterborough 019D       Stanground South       2261        2178      1880
## 6:        Swindon 008B Blunsdon and Highworth       2227        2166      2020
##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1:         Newham 013G Stratford and New Town        731        6351      6350
## 2:     Wandsworth 002B             Queenstown        675        3282      1700
## 3: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 4:         Newham 037E            Royal Docks        574        3116      2900
## 5:       Lewisham 012E       Lewisham Central        568        2893      2730
## 6:    Test Valley 003A                Alamein       2513        2638      2350
##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 2:    Test Valley 003B              St Mary's       2641        2487      2230
## 3:  Milton Keynes 017H              Broughton       2517        2382      2460
## 4:    Test Valley 003A                Alamein       2513        2638      2350
## 5:   Peterborough 019D       Stanground South       2261        2178      1880
## 6:        Swindon 008B Blunsdon and Highworth       2227        2166      2020

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values
Table 4.1: Data summary
Name …[]
Number of rows 32039
Number of columns 9
Key NULL
_______________________
Column type frequency:
numeric 9
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
CREDStotal_kgco2e_pdw 0 1 19490.24 9188.71 3587.62 12947.16 18275.82 24069.71 586372.22 ▇▁▁▁▁
CREDSgas_kgco2e2018_pdw 0 1 2465.42 851.99 3.92 2037.62 2434.68 2868.68 71095.56 ▇▁▁▁▁
CREDSelec_kgco2e2018_pdw 0 1 1021.63 220.17 40.55 888.82 977.15 1092.44 4046.23 ▂▇▁▁▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw 0 1 3487.05 912.74 458.61 2978.41 3398.85 3894.92 72698.53 ▇▁▁▁▁
CREDSotherEnergy_kgco2e2011_pdw 0 1 175.08 336.37 0.00 40.20 69.74 136.09 6877.09 ▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw 0 1 3662.13 910.23 912.57 3125.71 3558.65 4082.50 76436.03 ▇▁▁▁▁
CREDScar_kgco2e2018_pdw 0 1 2200.93 1038.37 127.70 1529.01 2142.16 2797.44 89700.00 ▇▁▁▁▁
CREDSvan_kgco2e2018_pdw 1 1 366.16 2774.12 0.05 137.01 217.71 342.60 344822.80 ▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw 1 1 2567.13 2987.05 141.80 1742.05 2422.45 3151.56 346819.80 ▇▁▁▁▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Scatter of LSOA level all per dwelling emissions against IMD score

Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -123.51, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5753081 -0.5604715
## sample estimates:
##        cor 
## -0.5679359
##    LSOA11CD            WD18NM          All_Tco2e_per_dw 
##  Length:32039       Length:32039       Min.   :  3.588  
##  Class :character   Class :character   1st Qu.: 12.947  
##  Mode  :character   Mode  :character   Median : 18.276  
##                                        Mean   : 19.490  
##                                        3rd Qu.: 24.070  
##                                        Max.   :586.372
##     LSOA11CD                  WD18NM All_Tco2e_per_dw
## 1: E01031998 Durrington and Larkhill         586.3722
## 2: E01009320                 Sheldon         364.6687
## 3: E01033484               Park East         203.6630
## 4: E01010151                  Knowle         171.2150
## 5: E01019556              Holmebrook         160.1703
## 6: E01033749               Greenbank         139.6909
##     LSOA11CD                 WD18NM All_Tco2e_per_dw
## 1: E01004562             Queenstown         4.965387
## 2: E01005133      Ancoats & Beswick         4.906386
## 3: E01008703                 Hendon         4.369222
## 4: E01015895               Victoria         4.289301
## 5: E01033726            Eltham West         3.808630
## 6: E01033583 Stratford and New Town         3.587624

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     3.92  2037.62  2434.68  2465.42  2868.68 71095.56
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Scatter of LSOA level gas per dwelling emissions against IMD score

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -70.089, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3740796 -0.3550910
## sample estimates:
##        cor 
## -0.3646232

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4069829 -0.3885483
## sample estimates:
##        cor 
## -0.3978058

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4069829 -0.3885483
## sample estimates:
##        cor 
## -0.3978058
##                                              RUC11 mean_gas_kgco2e mean_elec_kgco2e
## 1:                           Rural town and fringe        2536.798        1083.0125
## 2:       Rural town and fringe in a sparse setting        2254.050         993.6811
## 3:                     Rural village and dispersed        1879.326        1481.8790
## 4: Rural village and dispersed in a sparse setting        1015.146        1405.2387
## 5:                             Urban city and town        2456.035         991.9263
## 6:         Urban city and town in a sparse setting        2230.231         945.0026
## 7:                         Urban major conurbation        2552.187         981.2844
## 8:                         Urban minor conurbation        2582.837         913.8924
##    mean_other_energy_kgco2e
## 1:                274.22605
## 2:                271.63854
## 3:               1131.91956
## 4:               1440.13693
## 5:                 86.29202
## 6:                124.64526
## 7:                108.70527
## 8:                123.97196

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 158.14, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6559143 0.6682142
## sample estimates:
##       cor 
## 0.6621088
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 177.83, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6992585 0.7102801
## sample estimates:
##       cor 
## 0.7048118
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

How does the correlation look now?

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Scatter of LSOA level car use per dwelling emissions against IMD score

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -119.05, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5613723 -0.5461891
## sample estimates:
##        cor 
## -0.5538267
##                                              RUC11 mean_car_kgco2e mean_van_kgco2e
## 1:                           Rural town and fringe        2882.600        412.7957
## 2:       Rural town and fringe in a sparse setting        2198.057        346.9746
## 3:                     Rural village and dispersed        3754.901        664.4004
## 4: Rural village and dispersed in a sparse setting        3095.886        586.5956
## 5:                             Urban city and town        2280.407        379.2851
## 6:         Urban city and town in a sparse setting        1761.591        300.1992
## 7:                         Urban major conurbation        1718.983              NA
## 8:                         Urban minor conurbation        1899.379        307.5766

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Scatter of LSOA level van use per dwelling emissions against IMD score

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = -0.79155, df = 32036, p-value = 0.4286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.015371712  0.006528074
## sample estimates:
##          cor 
## -0.004422349

4.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    30.0   315.0   390.0   434.2   503.0  6350.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    36.0   623.0   692.0   736.3   809.0  6351.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.3 Estimating the annual emissions levy

Case studies:

  • Annual carbon tax
  • Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

EU carbon ‘price’

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

  • based on a Marginal Abatement Cost (MAC)
  • 2021:
    • Low: £122/T
    • Central: £245/T <- use the central value for now
    • High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.3.1 Scenario 1: Central cost

The table below shows the overall £ GBP total for the case study area in £M.

## £m
##    nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:  32039        107100.4          13847.3               5871.4
## £m
##                      region nLSOAs beis_GBPtotal_c beis_total_c_gas
## 1:               South East   5278       20772.109        2272.0667
## 2:                   London   4826       19038.076        2048.9049
## 3:               North West   4463       12703.630        1952.4218
## 4:                     East   3392       12199.273        1450.0085
## 5:            West Midlands   3403       10191.715        1475.8156
## 6:               South West   3059        9784.332        1147.1720
## 7: Yorkshire and The Humber   3271        9495.299        1494.0830
## 8:            East Midlands   2713        8687.056        1249.5031
## 9:               North East   1634        4228.912         757.3235
##    beis_GBPtotal_c_elec
## 1:            1038.8330
## 2:             870.4882
## 3:             766.0786
## 4:             668.5684
## 5:             604.8308
## 6:             613.8662
## 7:             550.1641
## 8:             503.9008
## 9:             254.6698

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  4780                       600                        250
##    beis_GBPtotal_c_energy_perdw
## 1:                          850

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA revenue using BEIS central carbon price

Figure 4.7: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA revenue using BEIS central carbon price

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     879    3172    4478    4775    5897  143661
##     LSOA11CD          LSOA01NM                  WD18NM CREDStotal_kgco2e_pdw
## 1: E01031998    Wiltshire 045C Durrington and Larkhill              586372.2
## 2: E01009320   Birmingham 081F                 Sheldon              364668.7
## 3: E01033484   Darlington 008F               Park East              203663.0
## 4: E01010151     Solihull 026A                  Knowle              171215.0
## 5: E01019556 Chesterfield 010C              Holmebrook              160170.3
## 6: E01033749    Liverpool 042F               Greenbank              139690.9
##    beis_GBPtotal_c_perdw
## 1:             143661.19
## 2:              89343.84
## 3:              49897.44
## 4:              41947.69
## 5:              39241.73
## 6:              34224.27
##     LSOA11CD             LSOA01NM                 WD18NM CREDStotal_kgco2e_pdw
## 1: E01004562      Wandsworth 002B             Queenstown              4965.387
## 2: E01005133      Manchester 013D      Ancoats & Beswick              4906.386
## 3: E01008703      Sunderland 013B                 Hendon              4369.222
## 4: E01015895 Southend-on-Sea 010A               Victoria              4289.301
## 5: E01033726       Greenwich 034E            Eltham West              3808.630
## 6: E01033583          Newham 013G Stratford and New Town              3587.624
##    beis_GBPtotal_c_perdw
## 1:             1216.5198
## 2:             1202.0645
## 3:             1070.4593
## 4:             1050.8787
## 5:              933.1143
## 6:              878.9679

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     0.96   499.22   596.50   604.03   702.83 17418.41
##     LSOA11CD          LSOA01NM                      WD18NM gasTCO2e_pdw
## 1: E01031998    Wiltshire 045C     Durrington and Larkhill    71.095556
## 2: E01000213       Barnet 033F               Garden Suburb     7.355417
## 3: E01023812 Three Rivers 004A Chorleywood North & Sarratt     7.167803
## 4: E01023841 Three Rivers 011C        Moor Park & Eastbury     6.925828
## 5: E01004114       Sutton 025D                       Cheam     6.721036
## 6: E01023813 Three Rivers 004B Chorleywood North & Sarratt     6.718669
##    beis_GBPtotal_c_gas_perdw
## 1:                 17418.411
## 2:                  1802.077
## 3:                  1756.112
## 4:                  1696.828
## 5:                  1646.654
## 6:                  1646.074
##     LSOA11CD                          LSOA01NM                  WD18NM gasTCO2e_pdw
## 1: E01026645 King's Lynn and West Norfolk 002A              Brancaster  0.015725987
## 2: E01026718 King's Lynn and West Norfolk 004D             Valley Hill  0.014207424
## 3: E01027382               Northumberland 002D Norham and Islandshires  0.013286252
## 4: E01020864                County Durham 064G                Evenwood  0.013174354
## 5: E01032746                  Southampton 029F                 Bargate  0.012818095
## 6: E01020534                  West Dorset 003F           Maiden Newton  0.003918875
##    beis_GBPtotal_c_gas_perdw
## 1:                 3.8528667
## 2:                 3.4808188
## 3:                 3.2551318
## 4:                 3.2277167
## 5:                 3.1404333
## 6:                 0.9601244

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.934 217.760 239.403 250.299 267.648 991.327
##     LSOA11CD         LSOA01NM                      WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01000206      Barnet 033B               Garden Suburb      4.046235                   991.3275
## 2: E01030692   Runnymede 005D              Virginia Water      3.360000                   823.2000
## 3: E01030342   Elmbridge 018B Oxshott and Stoke D'Abernon      3.346058                   819.7842
## 4: E01030346   Elmbridge 016A  Weybridge St George's Hill      3.280690                   803.7690
## 5: E01004690 Westminster 019D Knightsbridge and Belgravia      2.875978                   704.6145
## 6: E01003465      Merton 002D                     Village      2.873194                   703.9325
##     LSOA11CD                    LSOA01NM                          WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01008777             Sunderland 026C                       St Chad's    0.50798472                 124.456256
## 2: E01024604                  Swale 014C                        St Ann's    0.48269076                 118.259237
## 3: E01002862 Kensington and Chelsea 014E                         Stanley    0.45468354                 111.397468
## 4: E01033736              Greenwich 004H              Woolwich Riverside    0.43406378                 106.345626
## 5: E01004739            Westminster 024E                       Tachbrook    0.33211144                  81.367302
## 6: E01010257                Walsall 007E Aldridge North and Walsall Wood    0.04054826                   9.934324

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   112.4   729.7   832.7   854.3   954.3 17811.1

4.2.3.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw
##         0%        25%        50%        75%       100% 
##   3587.624  12947.165  18275.816  24069.709 586372.222
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##           V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
##  1: 20.14367                  1579.554                 1305.5194                  685.5016                3570.575
##  2: 18.65717                  1579.554                 1305.5194                  139.9562                3025.030
##  3: 12.73055                  1553.127                    0.0000                    0.0000                1553.127
##  4: 19.87204                  1579.554                 1305.5194                  585.8127                3470.886
##  5: 28.94094                  1579.554                 1305.5194                 3914.1019                6799.175
##  6: 15.12282                  1579.554                  533.0367                    0.0000                2112.591
##  7: 20.44254                  1579.554                 1305.5194                  795.1884                3680.262
##  8: 21.00183                  1579.554                 1305.5194                 1000.4491                3885.523
##  9: 11.91349                  1453.446                    0.0000                    0.0000                1453.446
## 10: 27.68047                  1579.554                 1305.5194                 3451.5071                6336.581
Table 4.2: Data summary
Name …[]
Number of rows 32039
Number of columns 3
Key NULL
_______________________
Column type frequency:
numeric 3
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
V1 0 1 19.49 9.19 3.59 12.95 18.28 24.07 586.37 ▇▁▁▁▁
beis_GBPtotal_sc2_perdw 0 1 3727.91 3031.87 437.69 1579.54 2885.00 5011.43 211376.45 ▇▁▁▁▁
beis_GBPtotal_sc2 0 1 2528423.89 1653321.47 543095.20 1222262.12 2220433.98 3356216.64 84377314.16 ▇▁▁▁▁
##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:  32039      107100.4      81008.17

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:                130.9338                15.97393
## 2:                 82.7677                10.09766
## 3:                717.3671                87.51878
## 4:               1041.0619               127.00956
## 5:               2480.0943               248.59012
## 6:                793.2446                96.77584
##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw beis_GBPgas_sc2_h_perdw
## 1:                130.9338                15.97393                 0.00000                 0.00000
## 2:                 82.7677                10.09766                 0.00000                 0.00000
## 3:                717.3671                87.51878                 0.00000                 0.00000
## 4:               1041.0619               127.00956                 0.00000                 0.00000
## 5:               2480.0943               248.59012                97.27961                16.66576
## 6:                793.2446                96.77584                 0.00000                 0.00000
##    beis_GBPgas_sc2_perdw
## 1:              15.97393
## 2:              10.09766
## 3:              87.51878
## 4:             127.00956
## 5:             362.53549
## 6:              96.77584
## [1] 9086.681

## [1] 3997.045

## £m
##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP   sumPop
## 1:  32039                81008.17            9086.681             3997.045 54619583
## £m
##                      region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP  sumPop
## 1:               South East   5278               17507.695           1503.8874             766.6684 8973952
## 2:                   London   4826               15649.748           1389.3724             583.7890 8889572
## 3:                     East   3392                9526.267            939.0531             487.3759 5818700
## 4:               North West   4463                8777.331           1282.6361             491.5296 7236660
## 5:            West Midlands   3403                7270.355            976.6076             410.8372 5765703
## 6:               South West   3059                6809.964            653.9993             431.0802 5213266
## 7: Yorkshire and The Humber   3271                6504.827           1011.0844             342.8749 5405939
## 8:            East Midlands   2713                6247.711            827.1231             339.4664 4693551
## 9:               North East   1634                2714.274            502.9172             143.4232 2622240

4.2.4 Estimate retofit costs

  • from A-E <- £13,300
  • from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

## To retrofit D-E (£m)
## [1] 177847.9
## Number of dwellings: 13372024
## To retrofit F-G (£m)
## [1] 26752.52
## Number of dwellings: 998229
## To retrofit D-G (£m)
## [1] 204600.4
## To retrofit D-G (mean per dwelling)
## [1] 14163.45
##    meanPerLSOA_GBPm total_GBPm
## 1:         6.385981   204600.4
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

Totals

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.5.2 Scenario 2

Totals

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.09599  2.40262  3.17210  3.53055  4.44332 15.64765        1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##   0.7742  14.7584  16.8635  17.5709  19.2684 118.6634        1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Highest retofit sum cost
##      LSOA11CD            LSOA11NM                             WD18NM retrofitSum yearsToPay  epc_D_pc  epc_E_pc
##  1: E01019012       Cornwall 054E                       St Ives East    26389383   32.33181 0.2881890 0.2251969
##  2: E01018781       Cornwall 034B                    Rame Peninsular    22060172   49.74697 0.2993730 0.2664577
##  3: E01027840    Scarborough 002C                           Mulgrave    21959636   32.73446 0.2821317 0.2272727
##  4: E01021988       Tendring 018A                         Golf Green    21701517   36.61775 0.2955900 0.3313468
##  5: E01018766       Cornwall 028D Looe West, Lansallos and Lanteglos    21409249   46.57111 0.2181070 0.2716049
##  6: E01020541    West Dorset 002C                     Sherborne East    21066562   31.46956 0.3038793 0.3232759
##  7: E01026741  North Norfolk 004A                         High Heath    20793004   35.15440 0.2971888 0.2650602
##  8: E01019002       Cornwall 070B               Newlyn and Mousehole    20415414   45.01341 0.1710963 0.2807309
##  9: E01018982       Cornwall 057C                        Hayle North    20411151   40.50980 0.2675386 0.1819263
## 10: E01027374 Northumberland 003A                           Bamburgh    19563519   29.95735 0.3329532 0.2257697
##      epc_F_pc   epc_G_pc
##  1: 0.1314961 0.07086614
##  2: 0.2335423 0.10971787
##  3: 0.2163009 0.10031348
##  4: 0.1620977 0.14302741
##  5: 0.2935528 0.10973937
##  6: 0.2090517 0.06896552
##  7: 0.1994645 0.06827309
##  8: 0.3089701 0.17940199
##  9: 0.2092747 0.14030916
## 10: 0.1402509 0.05131129

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

4.2.6.2 Scenario 2

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.06524  2.83119  4.88954  5.92627  8.83376 31.42356        1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##   0.7742  14.7584  16.8635  17.5709  19.2684 118.6634        1
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.3 Compare scenarios

Comparing pay-back times for the two scenarios - who does the rising block tariff help?

x = y line shown for clarity

5 R environment

5.1 R packages used

  • base R (R Core Team 2016)
  • bookdown (Xie 2016a)
  • data.table (Dowle et al. 2015)
  • ggplot2 (Wickham 2009)
  • kableExtra (Zhu 2018)
  • knitr (Xie 2016b)
  • rmarkdown (Allaire et al. 2018)
  • skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.
Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.